// Copyright (C) 2018-2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//

#include "ngraph/op/min.hpp"
#include <ngraph/validation_util.hpp>
#include "itt.hpp"
#include "ngraph/op/util/evaluate_helpers.hpp"
#include "ngraph/runtime/host_tensor.hpp"
#include "ngraph/runtime/reference/min.hpp"
#include "ngraph/shape_util.hpp"

using namespace std;
using namespace ngraph;

namespace minop
{
    template <element::Type_t ET>
    bool evaluate(const HostTensorPtr& arg,
                  const HostTensorPtr& out,
                  const AxisSet& axes,
                  const bool keep_dims)
    {
        out->set_shape(reduce(arg->get_shape(), axes, keep_dims));
        runtime::reference::min(
            arg->get_data_ptr<ET>(), out->get_data_ptr<ET>(), arg->get_shape(), axes);
        return true;
    }

    bool evaluate_min(const HostTensorPtr& arg,
                      const HostTensorPtr& out,
                      const AxisSet& axes,
                      const bool keep_dims)
    {
        bool rc = true;
        switch (arg->get_element_type())
        {
            NGRAPH_TYPE_CASE(evaluate_min, i32, arg, out, axes, keep_dims);
            NGRAPH_TYPE_CASE(evaluate_min, i64, arg, out, axes, keep_dims);
            NGRAPH_TYPE_CASE(evaluate_min, u32, arg, out, axes, keep_dims);
            NGRAPH_TYPE_CASE(evaluate_min, u64, arg, out, axes, keep_dims);
            NGRAPH_TYPE_CASE(evaluate_min, f16, arg, out, axes, keep_dims);
            NGRAPH_TYPE_CASE(evaluate_min, f32, arg, out, axes, keep_dims);
        default: rc = false; break;
        }
        return rc;
    }
} // namespace minop

NGRAPH_RTTI_DEFINITION(op::v1::ReduceMin, "ReduceMin", 1, util::ArithmeticReductionKeepDims);

op::v1::ReduceMin::ReduceMin(const Output<Node>& arg,
                             const Output<Node>& reduction_axes,
                             bool keep_dims)
    : ArithmeticReductionKeepDims(arg, reduction_axes, keep_dims)
{
    constructor_validate_and_infer_types();
}

shared_ptr<Node> op::v1::ReduceMin::clone_with_new_inputs(const OutputVector& new_args) const
{
    NGRAPH_OP_SCOPE(v1_ReduceMin_clone_with_new_inputs);
    check_new_args_count(this, new_args);
    return make_shared<op::v1::ReduceMin>(new_args.at(0), new_args.at(1), get_keep_dims());
}

bool op::v1::ReduceMin::evaluate(const HostTensorVector& outputs,
                                 const HostTensorVector& inputs) const
{
    NGRAPH_OP_SCOPE(v1_ReduceMin_evaluate);
    NGRAPH_CHECK(validate_host_tensor_vector(inputs, 2));
    NGRAPH_CHECK(validate_host_tensor_vector(outputs, 1));

    const auto reduction_axes = get_normalized_axes_from_tensor(
        inputs[1], inputs[0]->get_partial_shape().rank(), get_friendly_name());

    return minop::evaluate_min(inputs[0], outputs[0], reduction_axes, get_keep_dims());
}

bool op::v1::ReduceMin::has_evaluate() const
{
    NGRAPH_OP_SCOPE(v1_ReduceMin_has_evaluate);
    switch (get_input_element_type(0))
    {
    case ngraph::element::i32:
    case ngraph::element::i64:
    case ngraph::element::u32:
    case ngraph::element::u64:
    case ngraph::element::f16:
    case ngraph::element::f32: return true;
    default: break;
    }
    return false;
}

bool op::v1::ReduceMin::evaluate_lower(const HostTensorVector& output_values) const
{
    if (!input_value(1).get_tensor().has_and_set_bound())
        return false;
    return default_lower_bound_evaluator(this, output_values);
}

bool op::v1::ReduceMin::evaluate_upper(const HostTensorVector& output_values) const
{
    if (!input_value(1).get_tensor().has_and_set_bound())
        return false;
    return default_upper_bound_evaluator(this, output_values);
}
